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Regulation Of Health-Related Artificial Intelligence In Medical Devices: The Canadian Story, Michael Da Silva, Colleen M. M. Flood, Matthew Herder
Regulation Of Health-Related Artificial Intelligence In Medical Devices: The Canadian Story, Michael Da Silva, Colleen M. M. Flood, Matthew Herder
Articles, Book Chapters, & Popular Press
Artificial Intelligence (AI) may transform Canadian healthcare. The hope is that AI will enable more accurate and efficient care, thereby solving many access, quality, and safety problems. Yet, despite this tantalizing prospect, there are risks of unsafe AI harming patients, algorithmic bias, and threats to privacy. This work begins analysis of whether applicable Canadian laws are up to the task of ensuring Canadians can benefit from effective health-related AI while minimizing AI-related risks. It focuses on Health Canada’s regulation of medical devices, a ‘first line of defence’ that decides which devices are safe, effective, and thus permitted for trade in …
Submission To Canadian Government Consultation On A Modern Copyright Framework For Ai And The Internet Of Things, Sean Flynn, Lucie Guibault, Christian Handke, Joan-Josep Vallbé, Michael Palmedo, Carys Craig, Michael Geist, Joao Pedro Quintais
Submission To Canadian Government Consultation On A Modern Copyright Framework For Ai And The Internet Of Things, Sean Flynn, Lucie Guibault, Christian Handke, Joan-Josep Vallbé, Michael Palmedo, Carys Craig, Michael Geist, Joao Pedro Quintais
Reports & Public Policy Documents
We are grateful for the opportunity to participate in the Canadian Government’s consultation on a modern copyright framework for AI and the Internet of Things. Below, we present some of our research findings relating to the importance of flexibility in copyright law to permit text and data mining (“TDM”). As the consultation paper recognizes, TDM is a critical element of artificial intelligence. Our research supports the adoption of a specific exception for uses of works in TDM to supplement Canada’s existing general fair dealing exception.
Empirical research shows that more publication of citable research takes place in countries with “open” …
A Modern Copyright Framework For Artificial Intelligence: Ip Scholars' Joint Submission To The Canadian Government Consultation, Carys Craig, Bita Amani, Sara Bannerman, Céline Castets-Renard, Pascale Chapdelaine, Lucie Guibault, Gregory R. Hagen, Cameron J. Hutchison, Ariel Katz, Alexandra Mogyoros, Graham Reynolds, Anthony D. Rosborough, Teresa Scassa, Myra Tawfik
A Modern Copyright Framework For Artificial Intelligence: Ip Scholars' Joint Submission To The Canadian Government Consultation, Carys Craig, Bita Amani, Sara Bannerman, Céline Castets-Renard, Pascale Chapdelaine, Lucie Guibault, Gregory R. Hagen, Cameron J. Hutchison, Ariel Katz, Alexandra Mogyoros, Graham Reynolds, Anthony D. Rosborough, Teresa Scassa, Myra Tawfik
Reports & Public Policy Documents
In response to the Canadian government consultation process on the modernization of the copyright framework launched in the summer 2021, we hereby present our analysis and recommendations concerning the interaction between copyright and artificial intelligence (AI). The recommendations herein reflect the shared opinion of the intellectual property scholars who are signatories to this brief. They are informed by many combined decades of study, teaching, and practice in Canadian and international intellectual property law.
In what follows, we explain:
- The importance of approaching the questions raised in the consultation with a firm commitment to maintaining the appropriate balance of rights …
Legal Risks Of Adversarial Machine Learning Research, Ram Shankar Siva Kumar, Jonathon Penney, Bruce Schneier, Kendra Albert
Legal Risks Of Adversarial Machine Learning Research, Ram Shankar Siva Kumar, Jonathon Penney, Bruce Schneier, Kendra Albert
Articles, Book Chapters, & Popular Press
Adversarial machine learning is the systematic study of how motivated adversaries can compromise the confidentiality, integrity, and availability of machine learning (ML) systems through targeted or blanket attacks. The problem of attacking ML systems is so prevalent that CERT, the federally funded research and development center tasked with studying attacks, issued a broad vulnerability note on how most ML classifiers are vulnerable to adversarial manipulation. Google, IBM, Facebook, and Microsoft have committed to investing in securing machine learning systems. The US and EU are likewise putting security and safety of AI systems as a top priority.
Now, research on adversarial …
Politics Of Adversarial Machine Learning, Kendra Albert, Jonathon Penney, Bruce Schneier, Ram Shankar Siva Kumar
Politics Of Adversarial Machine Learning, Kendra Albert, Jonathon Penney, Bruce Schneier, Ram Shankar Siva Kumar
Articles, Book Chapters, & Popular Press
In addition to their security properties, adversarial machine-learning attacks and defenses have political dimensions. They enable or foreclose certain options for both the subjects of the machine learning systems and for those who deploy them, creating risks for civil liberties and human rights. In this paper, we draw on insights from science and technology studies, anthropology, and human rights literature, to inform how defenses against adversarial attacks can be used to suppress dissent and limit attempts to investigate machine learning systems. To make this concrete, we use real-world examples of how attacks such as perturbation, model inversion, or membership inference …
Implementing User Rights For Research In The Field Of Artificial Intelligence: A Call For International Action, Sean Flynn, Christophe Geiger, Joao Pedro Quintais, Thomas Margoni, Matthew Sag, Lucie Guibault, Michael W. Carroll
Implementing User Rights For Research In The Field Of Artificial Intelligence: A Call For International Action, Sean Flynn, Christophe Geiger, Joao Pedro Quintais, Thomas Margoni, Matthew Sag, Lucie Guibault, Michael W. Carroll
Articles, Book Chapters, & Popular Press
Last year, before the onset of a global pandemic highlighted the critical and urgent need for technology-enabled scientific research, the World Intellectual Property Organization (WIPO) launched an inquiry into issues at the intersection of intellectual property (IP) and artificial intelligence (AI). We contributed comments to that inquiry, with a focus on the application of copyright to the use of text and data mining (TDM) technology. This article describes some of the most salient points of our submission and concludes by stressing the need for international leadership on this important topic. WIPO could help fill the current gap on international leadership, …
Ethical Testing In The Real World: Evaluating Physical Testing Of Adversarial Machine Learning, Kendra Albert, Maggie Delano, Jonathon Penney, Afsaneh Ragot, Ram Shankar Siva Kumar
Ethical Testing In The Real World: Evaluating Physical Testing Of Adversarial Machine Learning, Kendra Albert, Maggie Delano, Jonathon Penney, Afsaneh Ragot, Ram Shankar Siva Kumar
Articles, Book Chapters, & Popular Press
This paper critically assesses the adequacy and representativeness of physical domain testing for various adversarial machine learning (ML) attacks against computer vision systems involving human subjects. Many papers that deploy such attacks characterize themselves as “real world.” Despite this framing, however, we found the physical or real-world testing conducted was minimal, provided few details about testing subjects and was often conducted as an afterthought or demonstration. Adversarial ML research without representative trials or testing is an ethical, scientific, and health/safety issue that can cause real harms. We introduce the problem and our methodology, and then critique the physical domain testing …
Privacy And Legal Automation: The Dmca As A Case Study, Jonathon Penney
Privacy And Legal Automation: The Dmca As A Case Study, Jonathon Penney
Articles, Book Chapters, & Popular Press
Advances in artificial intelligence, machine learning, computing capacity, and big data analytics are creating exciting new possibilities for legal automation. At the same time, these changes pose serious risks for civil liberties and other societal interests. Yet, existing scholarship is narrow, leaving uncertainty on a range of issues, including a glaring lack of systematic empirical work as to how legal automation may impact people’s privacy and freedom. This article addresses this gap with an original empirical analysis of the Digital Millennium Copyright Act (DMCA), which today sits at the forefront of algorithmic law due to its automated enforcement of copyright …